{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from convert_cifar10_to_tfrecord import load_data\n",
    "from cifar10 import get_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_dir = '../data/cifar-10-batches-py'\n",
    "train_filenames = [\n",
    "    'data_batch_1',\n",
    "    'data_batch_2',\n",
    "    'data_batch_3',\n",
    "    'data_batch_4',\n",
    "    'data_batch_5'\n",
    "]\n",
    "test_filenames = [\n",
    "    'test_batch'\n",
    "]\n",
    "train_images, train_labels, test_images, test_labels = get_data(dataset_dir, train_filenames, test_filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50000, 32, 32, 3), (50000, 10))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape, train_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "classification = ['airplane',\n",
    "                  'automobile',\n",
    "                  'bird',\n",
    "                  'cat',\n",
    "                  'deer',\n",
    "                  'dog',\n",
    "                  'frog',\n",
    "                  'horse',\n",
    "                  'ship',\n",
    "                  'truck']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = truck\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = 102\n",
    "plt.imshow(train_images[index])\n",
    "idx = np.argmax(train_labels[index])\n",
    "print (\"y = \" + classification[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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